Least Squares Derivation—Simple Algebraic Simplification

In summary, the author went from the last step at the bottom of pg. 7 to the final equation (11) at the top of pg. 8 by removing the brackets and changing the sign for the first term of the brackets.
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Hey, PF

I'm reading the following derivation of least squares, and I'm trying to figure out how the author went from the last step at the bottom of pg. 7 to the final equation (11) at the top of pg. 8.

[http://isites.harvard.edu/fs/docs/icb.topic515975.files/OLSDerivation.pdf]

More specifically, why is the denominator a difference of two terms? Aren't the terms in the denominator summed in the prior step?

I would expect the answer to be

$$
b_1=\dfrac{\displaystyle \sum_{\textrm{i=1}}^{n}y_ix_{i}-\left(\frac{1}{n}\right)\left(\sum_{\textrm{i=1}}^{n}y_i\sum_{\textrm{i=1}}^{n}x_{i}\right)}{\displaystyle\sum_{\textrm{i=1}}^{n}x_{i}^2+\left(\frac{1}{n}\right)\left(\sum_{\textrm{i=1}}^{n}x_{i}\right)^{2}}
$$

Note: I'm no statistician, but I thought you guys might be more familiar with this derivation.
 
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  • #2
I think you are right. The step only "divides both sides of the equation by the quantity in the large brackets on the left side" as the text states.

So, the sign doesn't change and will be the sum of the two terms in the denominator as you wrote out above.
 
  • #3
But the the mistake is a few steps before that where the text reads "Multiplying out the last term on the right". The writer removes the brackets but only changes the sign for the first term of the brackets. It is supposed to be a minus sign in the denominator (as in a difference of the two terms).
 
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  • #4
titasB said:
But the the mistake is a few steps before that where the text reads "Multiplying out the last term on the right". The writer removes the brackets but only changes the sign for the first term of the brackets. It is supposed to be a minus sign in the denominator (as in a difference of the two terms).

Thank you, TitasB!
 
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1. What is the purpose of deriving least squares using simple algebraic simplification?

The purpose of deriving least squares using simple algebraic simplification is to find the line of best fit for a set of data points. This line minimizes the sum of the squared distances between the data points and the line, making it a useful tool for analyzing and predicting relationships between variables.

2. How is simple algebraic simplification used in the derivation of least squares?

Simple algebraic simplification is used in the derivation of least squares by manipulating the equations and terms involved in the calculation. This helps to simplify the equations and make the derivation process more straightforward and easier to understand.

3. Can simple algebraic simplification be used for any type of data set?

Yes, simple algebraic simplification can be used for any type of data set, as long as the relationship between the variables can be represented by a linear model. It is a universal method for deriving least squares and is widely used in various fields of science, including statistics and data analysis.

4. What are the benefits of using simple algebraic simplification in the derivation of least squares?

The benefits of using simple algebraic simplification in the derivation of least squares include making the equations and calculations easier to understand, reducing the complexity of the derivation process, and allowing for a more intuitive interpretation of the results. It also helps to streamline the process of finding the line of best fit and makes it easier to apply to different data sets.

5. Are there any limitations to using simple algebraic simplification in the derivation of least squares?

One of the limitations of using simple algebraic simplification in the derivation of least squares is that it can only be used for linear relationships between variables. If the data set does not have a linear relationship, then this method will not be applicable. Additionally, it may not provide the most accurate results when dealing with a large number of data points, as it is a simplified approach.

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